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GETTING_STARTED.md

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Getting Started

pycls can be used as a library (e.g. import pretrained models) or as a framework (e.g. modify for your needs). This document provides brief installation instructions and basic usage examples for both use cases.

Notes:

  • pycls has been tested with PyTorch 1.6, CUDA 9.2 and cuDNN 7.1
  • pycls currently does not support running on CPU; a GPU system is required

Library Usage

Install the package:

pip install pycls

Load a pretrained model:

model = pycls.models.regnetx("400MF", pretrained=True)

Create a model with the number of classes altered:

model = pycls.models.regnety("4.0GF", pretrained=False, cfg_list=("MODEL.NUM_CLASSES", 100))

Please see the MODEL_ZOO.md for the available pretrained models.

Framework Usage

Clone the repository:

git clone https://github.com/facebookresearch/pycls

Install dependencies:

pip install -r requirements.txt

Set all the files in ./tools to be executable by the user:

chmod 744 ./tools/*.py

Set up modules:

python setup.py develop --user

Please see DATA.md for the instructions on setting up datasets.

The examples below use a config for RegNetX-400MF on ImageNet with 8 GPUs.

Model Info

./tools/run_net.py --mode info \
    --cfg configs/dds_baselines/regnetx/RegNetX-400MF_dds_8gpu.yaml

Model Evaluation

./tools/run_net.py --mode test \
    --cfg configs/dds_baselines/regnetx/RegNetX-400MF_dds_8gpu.yaml \
    TEST.WEIGHTS https://dl.fbaipublicfiles.com/pycls/dds_baselines/160905967/RegNetX-400MF_dds_8gpu.pyth \
    OUT_DIR /tmp

Model Evaluation (multi-node)

 ./tools/run_net.py --mode test \
    --cfg configs/dds_baselines/regnetx/RegNetX-400MF_dds_8gpu.yaml \
    TEST.WEIGHTS https://dl.fbaipublicfiles.com/pycls/dds_baselines/160905967/RegNetX-400MF_dds_8gpu.pyth \
    OUT_DIR test/ LOG_DEST file LAUNCH.MODE slurm LAUNCH.PARTITION devlab NUM_GPUS 16 LAUNCH.NAME pycls_eval_test

Model Training

./tools/run_net.py --mode train \
    --cfg configs/dds_baselines/regnetx/RegNetX-400MF_dds_8gpu.yaml \
    OUT_DIR /tmp

Model Finetuning

./tools/run_net.py --mode train \
    --cfg configs/dds_baselines/regnetx/RegNetX-400MF_dds_8gpu.yaml \
    TRAIN.WEIGHTS https://dl.fbaipublicfiles.com/pycls/dds_baselines/160905967/RegNetX-400MF_dds_8gpu.pyth \
    OUT_DIR /tmp

Model Timing

./tools/run_net.py --mode time \
    --cfg configs/dds_baselines/regnetx/RegNetX-400MF_dds_8gpu.yaml \
    NUM_GPUS 1 \
    TRAIN.BATCH_SIZE 64 \
    TEST.BATCH_SIZE 64 \
    PREC_TIME.WARMUP_ITER 5 \
    PREC_TIME.NUM_ITER 50

Model Scaling

Scale a RegNetY-4GF by 4x using fast compound scaling (see https://arxiv.org/abs/2103.06877):

./tools/run_net.py --mode scale \
    --cfg configs/dds_baselines/regnety/RegNetY-4.0GF_dds_8gpu.yaml \
    OUT_DIR ./ \
    CFG_DEST "RegNetY-4.0GF_dds_8gpu_scaled.yaml" \
    MODEL.SCALING_FACTOR 4.0 \
    MODEL.SCALING_TYPE "d1_w8_g8_r1"